One area of computer vision is feature matching, where a feature in a field of view of one camera is tracked over time, or a feature in the field of view of multiple cameras is tracked at the same point in time. Finding the set of pixels representing a particular feature to be tracked, such as the corner of a book shelf, in different views of a scene is key to creating a 3d reconstruction of a scene. For corner features descriptor-based matching has proven vastly superior over previous approaches and a rich body of approaches exist (e.g. SIFT, SURF). More recent corner descriptors are capable to work in super real-time (e.g. BRIEF).
However, corner features and/or rich textures are oftentimes not present in the quantity required for 3d reconstruction in certain environments, such as for example a non-cluttered living room. Such environments often still do include long straight edge features. For example, a living room may include bookshelves, tables, ceilings and walls). For edge features, however, no robust and super real-time feature description or matching algorithm has been proposed yet.